1 citations · 2 across the 5 of their papers we have counts for
6 papers
Towards Financial World Modeling
Humzah Merchant, Alec Guthrie, Simon Mahns +2
Building a world model requires a state representation useful for planning and decision-making---potentially over tasks unknown at training time. In the context of financial market…
Forecasting With LLMs: Improved Generalization Through Feature Steering
Humzah Merchant, Bradford Levy
Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply LLMs to a variety of foreca…
Divergence Decoding: Inference-Time Unlearning via Auxiliary Models
Humzah Merchant, Bradford Levy
Large Language Models (LLMs) frequently memorize sensitive training data thereby creating significant privacy and copyright risks. Addressing these risks, i.e., removing such knowl…
Evaluating LLMs in Finance Requires Explicit Bias Consideration
Yaxuan Kong, Hoyoung Lee, Yoontae Hwang +7
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contami…
A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs
Humzah Merchant, Bradford Levy
Applying LLMs to predictive tasks in finance is challenging due to look-ahead bias resulting from their training on long time-series data. This precludes the backtests typically em…
BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text
Siyan Wang, Bradford Levy
Many of the recent breakthroughs in language modeling have resulted from scaling effectively the same model architecture to larger datasets. In this vein, recent work has highlight…